Annotating multiple organs in medical images is both costly and time-consuming; therefore,
existing multi-organ datasets with labels are often low in sample size and mostly partially …

Shortage of fully annotated datasets has been a limiting factor in developing deep learning
based image segmentation algorithms and the problem becomes more pronounced in multi …

Multi-organ segmentation requires to segment multiple organs of interest from each image.
However, it is generally quite difficult to collect full annotations of all the organs on the same …

Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel
level, most benchmark datasets are equipped with the annotations of only one type of …

Simultaneous segmentation of multiple organs from different medical imaging modalities is a
crucial task as it can be utilized for computer-aided diagnosis, computer-assisted surgery …

Accurate multi-organ abdominal CT segmentation is essential to many clinical applications
such as computer-aided intervention. As data annotation requires massive human labor …

We propose a novel teacher-student model for semi-supervised multi-organ segmentation.
In the teacher-student model, data augmentation is usually adopted on unlabeled data to …

Automatic segmentation of organs at risk is crucial to aid diagnoses and remains a
challenging task in medical image analysis domain. To perform the segmentation, we use …

Multi-organ segmentation is a challenging task due to the label imbalance and structural
differences between different organs. In this work, we propose an efficient cascaded V-Net …

Accurate and robust segmentation of abdominal organs on CT is essential for many clinical
applications such as computer-aided diagnosis and computer-aided surgery. But this task is …